An Iterative Estimation Algorithm for a Class of Wiener System Model Utilizing a Piece-Wise Linear Approximation

In this paper, we develop a Maximum likelihood estimation algorithm for a class of Wiener system written in a linear regression form. The output non-linearity is approximated by utilizing a piece-wise linear function. An iterative algorithm is proposed to estimate the linear regression model and the parameters of the piece-wise linear approximation. Closed-form expressions to estimate parameters are obtained. The benefits of our proposal are illustrated via numerical simulations.